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Genetic Fuzzy System for Automating Maritime Risk Assessment

  • Alexander TeskeEmail author
  • Rafael Falcon
  • Rami Abielmona
  • Emil Petriu
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 377)

Abstract

This chapter uses genetic fuzzy systems (GFS) to assess the risk level of maritime vessels transmitting Automatic Identification System (AIS) data. Previous risk assessment approaches based on fuzzy inference systems (FIS) relied on domain experts to specify the FIS membership functions as well as the fuzzy rule base (FRB), a burdensome and time-consuming process. This chapter aims to alleviate this burden by learning the membership functions and FRB for the FIS of an existing Risk Management Framework (RMF) directly from data. The proposed methodology is tested with four different case studies in maritime risk analysis. Each case study concerns a unique scenario involving a particular region: the Gulf of Guinea, the Strait of Malacca, the Northern Atlantic during a storm, and the Northern Atlantic during a period of calm seas. The experiments compare 14 GFS algorithms from the KEEL software package and evaluate the resulting FRBs according to their accuracy and interpretability. The results indicate that IVTURS, LogitBoost, and NSLV generate the most accurate rule bases while SGERD, GCCL, NSLV, and GBML each generate interpretable rule bases. Finally, IVTURS, NSLV, and GBML algorithms offer a reasonable compromise between accuracy and interpretability.

Keywords

Maritime domain awareness Risk management Genetic algorithms Fuzzy systems Multi-objective optimization 

Notes

Acknowledgements

The authors acknowledge the financial support of the Ontario Centres of Excellence (OCE) and the National Sciences and Engineering Research Council of Canada (NSERC) for the project entitled “Big Data Analytics for the Maritime Internet of Things”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander Teske
    • 1
    Email author
  • Rafael Falcon
    • 1
    • 2
  • Rami Abielmona
    • 1
    • 2
  • Emil Petriu
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Research & Engineering DivisionLarus Technologies CorporationOttawaCanada

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